Comparing Azure Data Factory with Estuary and Weld
Carolina Russ6 min read
What is Azure Data Factory
Azure Data Factory (ADF) is Microsoft’s cloud-based data integration service for creating ETL/ELT pipelines. ADF supports a drag-and-drop pipeline designer, over 90 built-in connectors for Azure, on-premises, and SaaS data sources, and can execute transformations via Azure Databricks, U-SQL, or stored procedures. It also includes features for data orchestration, monitoring, and hybrid data integration scenarios.
Pros
- 90+ built-in connectors (Azure SQL, Cosmos DB, SAP, Oracle, Salesforce, etc.) and support for custom REST endpoints.
- Visual pipeline orchestration with debug, parameterization, and Git integration for CI/CD.
- Hybrid data integration via Self-hosted Integration Runtime for on-premises sources.
- Integration with Azure Synapse, Databricks, and Azure Functions for flexible transformation and compute.
Cons
- Complex pricing: charges per pipeline activity, per DIU for data flows, and for data movement across regions.
- UI can be slow when working with large pipelines; error messages are often generic, requiring deeper investigation.
- Steeper learning curve for advanced features (e.g., mapping data flows with Spark under the hood).
Azure Data Factory Documentation:
What I like about Azure Data Factory
ADF’s visual pipeline authoring and integration with other Azure services (Databricks, Synapse) make it easy to build end-to-end data workflows without managing infrastructure.
What I dislike about Azure Data Factory
Pricing is multifaceted (per activity run, data movement, SSIS integration), which can be hard to forecast. Debugging pipeline errors often requires sifting through activity logs.
What is Estuary
Estuary Flow is a real-time ETL/ELT and data integration platform for both batch and streaming pipelines. It provides sub-100ms latency using Change Data Capture (CDC), supports automated schema evolution, and allows users to build entire pipelines with low- or no-code connectors in minutes. It can target data warehouses (e.g., Snowflake, BigQuery), BI tools, and operational systems for analytics, operations, and AI use cases.
Pros
- Purpose-built for real-time CDC and streaming ETL with sub-100ms latency.
- Automatic schema evolution with exactly-once delivery guarantees.
- 200+ no-code connectors for databases, SaaS apps, and message queues.
- Flexible deployment: public cloud, private cloud, or self-hosted (BYOC).
Cons
- Premium pricing model ($0.50/GB consumed + connector fees) can be expensive for small teams.
- Still growing connector catalog; niche or very new APIs may require custom work.
- Smaller community compared to older open-source tools, meaning fewer community-built resources.
Estuary Pricing Page:
What I like about Estuary
Estuary’s real-time, no-code model is magical—getting data instantly with minimal effort and near-zero pipeline maintenance. Plus, their support is fantastic.
What I dislike about Estuary
Pricing can be high for lower-volume teams, and some less-common connectors are still in development, which limits immediate use cases for niche sources.
What is Weld
Weld is a powerful ETL platform that seamlessly integrates ELT, data transformations, reverse ETL, and AI-assisted features into one user-friendly solution. With its intuitive interface, Weld makes it easy for anyone, regardless of technical expertise, to build and manage data workflows. Known for its premium quality connectors, all built in-house, Weld ensures the highest quality and reliability for its users. It is designed to handle large datasets with near real-time data synchronization, making it ideal for modern data teams that require robust and efficient data integration solutions. Weld also leverages AI to automate repetitive tasks, optimize workflows, and enhance data transformation capabilities, ensuring maximum efficiency and productivity. Users can combine data from a wide variety of sources, including marketing platforms, CRMs, e-commerce platforms like Shopify, APIs, databases, Excel, Google Sheets, and more, providing a single source of truth for all their data.
Pros
- Premium quality connectors and reliability
- User-friendly and easy to set up
- AI assistant
- Very competitive and easy-to-understand pricing model
- Reverse ETL option
- Lineage, orchestration, and workflow features
- Advanced transformation and SQL modeling capabilities
- Ability to handle large datasets and near real-time data sync
- Combines data from a wide range of sources for a single source of truth
Cons
- Requires some technical knowledge around data warehousing and SQL
- Limited features for advanced data teams
A reviewer on G2 said:
What I like about Weld
First and foremost, Weld is incredibly user-friendly. The graphical interface is intuitive, which makes it easy to build data workflows quickly and efficiently. Even with little experience in SQL and pipeline management, we found that Weld was straightforward and easy to use. What really impressed me, however, was Weld's flexibility. It was able to handle data from a wide variety of sources, including SQL databases, Google Sheets, and even APIs. The solution also allowed us to customize my data transformations in a way that best suited my needs. Whether I needed to clean data, join tables, or aggregate data, Weld had the necessary tools to accomplish the task. Weld's performance was also exceptional. I was able to run large-scale ETL jobs quickly and efficiently, with minimal downtime via a Snowflake instance and visualization via own-hosted Metabase. The solution's scalability meant that I could process more data without any issues. Another standout feature of Weld was its support. I never felt lost or unsure about how to use a particular feature, as the support team was always quick to respond to any questions or concerns that I had. Overall, I highly recommend Weld as an ETL solution. Its user-friendliness, flexibility, performance, and support make it an excellent choice for anyone looking to streamline their data integration processes. I will definitely be using Weld for all my ETL needs going forward.
What I dislike about Weld
Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.
Azure Data Factory vs Estuary: Ease of Use and User Interface
Azure Data Factory
ADF’s UI provides a canvas for building pipelines and data flows. Basic data movement is intuitive, but advanced mapping data flows (visual Spark transformations) require understanding Spark concepts. Integration with Git makes collaboration easier.
Estuary
Estuary’s UI is intuitive: users can add connectors, configure CDC streams, and specify destinations in a few clicks. Complex transformations can be written in SQL or TypeScript directly in the Flow editor, but most tasks are handled via no-code connectors.
Azure Data Factory vs Estuary: Pricing Transparency and Affordability
Azure Data Factory
ADF charges per pipeline activity (at least $0.25/activity), per DIU-hour for data flows, plus data movement costs (e.g., $0.25/GB). Estimating costs can be tricky due to these components, but pay-as-you-go avoids upfront fees.
Estuary
While Estuary provides a 10 GB/month free tier and a 30-day trial, its consumption-based pricing ($0.50/GB + connector fees) can become costly at scale. Teams processing hundreds of GBs per month should budget accordingly.
Azure Data Factory vs Estuary: Comprehensive Feature Set
Azure Data Factory
Features include: pipeline orchestration, mapping data flows (visual Spark jobs), hybrid integration via self-hosted runtime, triggers (schedule, event, tumbling window), monitoring & alerting, and integration with Azure Monitor. Also supports SSIS lift-and-shift for on-prem ETL workloads.
Estuary
Key features include real-time CDC (sub-100ms latency), batch and streaming pipelines, automated schema evolution, and in-stream or post-load transformations via SQL/TypeScript or dbt. It also supports Kafka-compatibility and private storage for data replay.
Azure Data Factory vs Estuary: Flexibility and Customization
Azure Data Factory
ADF allows custom .NET activities, Azure Functions, and Databricks notebooks within pipelines. It supports parameterized templates, branching, and custom Azure ML scoring steps. However, customization often requires familiarity with other Azure services.
Estuary
Estuary allows custom TypeScript transforms in-stream or SQL in-destination. Pipelines can be managed via CLI (flowctl) and integrated into CI/CD. While most connectors are no-code, custom connectors can be built using the open-source Flow SDK.
Summary of Azure Data Factory vs Estuary vs Weld
| Weld | Azure Data Factory | Estuary | |
|---|---|---|---|
| Connectors | 200++ | 90+ | 200+ |
| Price | $99 / Unlimited usage | Pay per activity run + data movement; starts ~$0.25 per DIU-hour for data flows | $0.50/GB consumed + per-connector fee |
| Free tier | No | Yes | Yes |
| Location | EU | Azure Global (multi-region) | New York, NY, USA |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | No | No |
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| On-Premise | No | No | Yes |
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | Yes | Yes |
| Version control | Yes | Yes | Yes |
| Load data to and from Excel | Yes | Yes | No |
| Load data to and from Google Sheets | Yes | No | Yes |
| Two-Way Sync | Yes | No | No |
| dbt Core Integration | Yes | No | Yes |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | No | Yes |
| G2 Rating | 4.8 | 4.4 | 4.8 |
Conclusion
You’re comparing Azure Data Factory, Estuary, Weld. Each of these tools has its own strengths:
- Azure Data Factory: features include: pipeline orchestration, mapping data flows (visual spark jobs), hybrid integration via self-hosted runtime, triggers (schedule, event, tumbling window), monitoring & alerting, and integration with azure monitor. also supports ssis lift-and-shift for on-prem etl workloads. . adf charges per pipeline activity (at least $0.25/activity), per diu-hour for data flows, plus data movement costs (e.g., $0.25/gb). estimating costs can be tricky due to these components, but pay-as-you-go avoids upfront fees. .
- Estuary: key features include real-time cdc (sub-100ms latency), batch and streaming pipelines, automated schema evolution, and in-stream or post-load transformations via sql/typescript or dbt. it also supports kafka-compatibility and private storage for data replay. . while estuary provides a 10 gb/month free tier and a 30-day trial, its consumption-based pricing ($0.50/gb + connector fees) can become costly at scale. teams processing hundreds of gbs per month should budget accordingly. .
- Weld: weld integrates elt, data transformations, and reverse etl all within one platform. it also provides advanced features such as data lineage, orchestration, workflow management, and an ai assistant, which helps in automating repetitive tasks and optimizing workflows.. weld offers a straightforward and competitive pricing model, starting at $99 for 2 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises..
